Integration of Fault Analysis and Interlock Controller Synthesis for Batch Processes

在各种各样的决策之中的集成处理，例如计划，图案，和操作对动态、灵活的批生产必要。完成批生产集成，为各种各样的决策过程使用的普通模型的利用是一条有效途径。从这个观点，一个批系统由一张 Petri 网描述了的普通模型被建议。在这篇文章，为批进程的一种故障诊断技术用差错繁殖和差错分析的集成的可能性的信息被介绍，控制器合成根据 Petri 网络被讨论基于的普通模型。

Just-in-time learning based integrated MPC-ILC control for batch processes

Considering the two-dimension(2 D) characteristic and the unknown optimal trajectory problem of the batch processes, an integrated model predictive control-iterative learning control(MPC-ILC) for batch processes is proposed in this paper. Firstly, the batch-axis information and time-axis information are combined into one quadratic performance index. It implies the integration of ILC and MPC algorithm idea, which leads to superior tracking performance and better robustness against disturbance and uncertainty. To address the problem of the unknown optimal trajectory, both time-varying prediction horizon and end product quality control are employed. Moreover, an integrated 2 D just-in-time learning(JITL) model is used to improve the predictive accuracy. Furthermore, rigorous description and proof are presented to prove the convergence and tracking performance of the proposed MPC-ILC strategy. The simulation results show the effectiveness of the proposed method.

An Anticipatory Terminal Iterative Learning Control Approach with Applications to Constrained Batch Processes

This work presents an anticipatory terminal iterative learning control scheme for a class of batch processes, where only the final system output is measurable and the control input is constant in each operations. The proposed approach works well with input constraints provided that the desired control input with respect to the desired trajectory is within the saturation bound. The tracking error convergence is established with rigorous mathematical analysis. Simulation results are provided to show the effectiveness of the proposed approach.

Studies on Composting Spent Coffee Grounds by <i>Aspergillus sp</i>and <i>Aspergillus sp</i>in Aerobic Static Batch Temperature Control

Spent Coffee Ground (SCG) is characterized by high organic content, in the form of insoluble polysaccharides bound and phenol compounds. Phenol compounds are toxic to nature and are a cause of environmental pollution. Composting method of this study is aerobic static batch composting with temperature control with adding activators of some fungi such as Aspergillus sp, and Penicillium sp. The purpose of the research is to fill the research gap from previous studies of spent coffee grounds compost, which requires a long time in composting, so that if it is used directly on the soil and plants, the positive effect also requires a long time. The result of composting for 28 days with this method is that mature compost has black crumb and normal pH, with characteristics of C/N ratio below 10: C1 (7.06), C2 (6.99). This value is far from the control with a C/N ratio of 8.33. Decompose rate of macromolecule are above 40% for lignin and 70% for cellulose. Implementation of compost in radish plants, resulting Germination Index above 80% which indicates that the compost is ripe: control (92.39%), C1 (183.88%), C2 (191.86%). The results of the analysis with FTIR also showed that the compost was mature and stable, and rich in minerals. So, it can be concluded that this composting method can speed up composting time and optimize the results of compost produced.

Modelling and Multi-Objective Optimal Control of Batch Processes Using Recurrent Neuro-fuzzy Networks

In this paper, the modelling and multi-objective optimal control of batch processes, using a recurrent neuro-fuzzy network, are presented. The recurrent neuro-fuzzy network, forms a ＂global＂ nonlinear long-range prediction model through the fuzzy conjunction of a number of ＂local＂ linear dynamic models. Network output is fed back to network input through one or more time delay units, which ensure that predictions from the recurrent neuro-fuzzy network are long-range. In building a recurrent neural network model, process knowledge is used initially to partition the processes non-linear characteristics into several local operating regions, and to aid in the initialisation of corresponding network weights. Process operational data is then used to train the network. Membership functions of the local regimes are identified, and local models are discovered via network training. Based on a recurrent neuro-fuzzy network model, a multi-objective optimal control policy can be obtained. The proposed technique is applied to a fed-batch reactor.

Iterative Learning Model Predictive Control for a Class of Continuous/Batch Processes

一个反复的学习模型预兆的控制(ILMPC ) 技术被用于 continuous/batch 过程的一个班。如此的过程被产生周期的强壮的骚乱到连续过程的批过程的操作描绘，传统的规章的控制器是不能的消除这些周期的骚乱。ILMPC 集成处理重复信号和灵活性的反复的学习控制(ILC ) 的特征为预兆的控制(MPC ) 建模。由联机监视，批的操作地位处理，事件驱动为批的反复的学习算法重复骚乱被开始，软限制被调整象可行区域及时离开需要的操作地区。一个工业应用程序的结果证明建议 ILMPC 方法为 continuous/batch 进程的一个类是有效的。

An LMI Method to Robust Iterative Learning Fault-tolerant Guaranteed Cost Control for Batch Processes

Based on an equivalent two-dimensional Fornasini-Marchsini model for a batch process in industry, a closed-loop robust iterative learning fault-tolerant guaranteed cost control scheme is proposed for batch processes with actuator failures. This paper introduces relevant concepts of the fault-tolerant guaranteed cost control and formulates the robust iterative learning reliable guaranteed cost controller (ILRGCC). A significant advantage is that the proposed ILRGCC design method can be used for on-line optimization against batch-to-batch process uncertainties to realize robust tracking of set-point trajectory in time and batch-to-batch sequences. For the convenience of implementation, only measured output errors of current and previous cycles are used to design a synthetic controller for iterative learning control, consisting of dynamic output feedback plus feed-forward control. The proposed controller can not only guarantee the closed-loop convergency along time and cycle sequences but also satisfy the H∞performance level and a cost function with upper bounds for all admissible uncertainties and any actuator failures. Sufficient conditions for the controller solution are derived in terms of linear matrix inequalities (LMIs), and design procedures, which formulate a convex optimization problem with LMI constraints, are presented. An example of injection molding is given to illustrate the effectiveness and advantages of the ILRGCC design approach.

2D multi-model general predictive iterative learning control for semi-batch reactor with multiple reactions

Batch to batch temperature control of a semi-batch chemical reactor with heating/cooling system was discussed in this study. Without extensive modeling investigations, a two-dimensional(2D) general predictive iterative learning control(2D-MGPILC) strategy based on the multi-model with time-varying weights was introduced for optimizing the tracking performance of desired temperature profile. This strategy was modeled based on an iterative learning control(ILC) algorithm for a 2D system and designed in the generalized predictive control(GPC) framework. Firstly, a multi-model structure with time-varying weights was developed to describe the complex operation of a general semi-batch reactor. Secondly, the 2 D-MGPILC algorithm was proposed to optimize simultaneously the dynamic performance along the time and batch axes. Finally, simulation for the controller design of a semi-batch reactor with multiple reactions was involved to demonstrate that the satisfactory performance could be achieved despite of the repetitive or non-repetitive disturbances.

Optimal Iterative Learning Control for Batch Processes Based on Linear Time-varying Perturbation Model

为在批的产品质量的追踪的控制的最佳的反复的学习控制(ILC ) 策略处理的 batch-to-batch 被介绍。线性变化时间的不安(LTVP ) 模型在名字的轨道附近为产品质量被造。到模型植物失配的地址问题，在以前的成批处理的模型预言错误为当前的成批处理被加到模型预言。然后追踪错误转变模型能被造，并且有直接错误反馈的 ILC 法律明确地被获得。一条严密定理被建议，到证明在 ILC 下面追踪错误的集中。建议方法论在一个典型的批反应堆和轨道追踪的表演被 ILC 逐渐地改进的结果表演上被说明。

Design and control of a middle-vessel batch distillation for separating DMC–EMC–DEC mixture

In this paper, the mixture of dimethyl carbonate, ethyl methyl carbonate and diethyl carbonate was separated by middle-vessel batch distillation with feeding in middle-vessel and process control characteristics were researched. The steady state simulation results in Aspen Plus were exported to Aspen Dynamics. Then control effect of liquid level control with HighSelector, composition control(structure1, structure2) and temperature control(proportional action, proportional integration action) were proposed. Composition control structure 2 and temperature control with PI action were investigated to achieve a good control effect.

Batch Process Modelling and Optimal Control Based on Neural Network Model

This paper presents several neural network based modelling, reliable optimal control, and iterative learning control methods for batch processes. In order to overcome the lack of robustness of a single neural network, bootstrap aggregated neural networks are used to build reliable data based empirical models. Apart from improving the model generalisation capability, a bootstrap aggregated neural network can also provide model prediction confidence bounds. A reliable optimal control method by incorporating model prediction confidence bounds into the optimisation objective function is presented. A neural network based iterative learning control strategy is presented to overcome the problem due to unknown disturbances and model-plant mismatches. The proposed methods are demonstrated on a simulated batch polymerisation process.

Robustness of reinforced gradient-type iterative learning control for batch processes with Gaussian noise

In this paper,a reinforced gradient-type iterative learning control pro file is proposed by making use of system matrices and a proper learning step to improve the tracking performance of batch processes disturbed by external Gaussian white noise.The robustness is analyzed and the range of the step is speci fied by means of statistical technique and matrix theory.Compared with the conventional one,the proposed algorithm is more ef ficient to resist external noise.Numerical simulations of an injection molding process illustrate that the proposed scheme is feasible and effective.

Re-Testing in Batch Testing Model Based on Quality Control Process for Proportion Estimation

The quality of products manufactured or procured by organizations is an important aspect of their survival in the global market. The quality control processes put in place by organizations can be resource-intensive but substantial savings can be realized by using acceptance sampling in conjunction with batch testing. This paper considers the batch testing model based on the quality control process where batches that test positive are re-tested. The results show that re-testing greatly improves the efficiency over one stage batch testing based on quality control. This is observed using Asymptotic Relative Efficiency (ARE), where for values of p computed ARE > 1 implying that our estimator has a smaller variance than the one-stage batch testing. Also, it was found that the model is more efficient than the classical two-stage batch testing for relatively high values of proportion.

Run-to-run product quality control of batch processes

Batch processes have been increasingly used in the production of low volume and high value added products. Consequently, optimization control in batch processes is crucial in order to derive the maximum benefit. In this paper, a run-to-run product quality control based on iterative learning optimization control is developed. Moreover, a rigorous theorem is proposed and proven in this paper, which states that the tracking error under the optimal iterative learning control （ILC） law can converge to zero. In this paper, a typical nonlinear batch continuous stirred tank reactor （CSTR） is considered, and the results show that the performance of trajectory tracking is gradually improved by the ILC.

Predictive Sliding Mode Control to a Thermal Process for Batch Dyeing

The batch dyeing process is a typical nonlinear process with time-delay,where precise controlling of temperature plays a vital role on the dyeing quality.Because the accuracy and robustness of the commonly used proportion integration differentiation(PID) algorithm had been limited,a novel method was developed to precisely control the heating and cooling stages for batch dyeing process based on predictive sliding mode control(SMC) algorithm.Firstly,a special predictive sliding mode model was constructed according to the principle of generalized predictive control(GPC);secondly,an appropriate reference trajectory for SMC was designed based on the improved approaching law;finally,the predictive sliding mode model and the Diophantine equation were used to predict the output and then the optimized control law was derived using the generalized predictive law.This method combined GPC and the SMC with their respective advantages,so it could be applied to time-delay process,making the control system more robust.Simulation experiments show that this algorithm can well track the temperature variation for the batch dyeing process.

Model-Based Split-Range Algorithm for the Temperature Control of a Batch Reactor

In the manufacturing processes of high value-added products in the pharmaceutical, fine chemical polymer and food industry, insufficient control might produce off-grade products. This can cause significant financial losses, or in the pharmaceutical industry, it can result in an unusable batch. In these industries, batch reactors are commonly used, the control of which is essentially a problem of temperature control. In the industry, an increasing number of heating-cooling systems utilising three different temperature levels can be found, which are advantageous from an economic point of view. However, it makes the control more complicated. This paper presents a split-range designing technique using the model of the controlled system with the aim to design a split-range algorithm more specific to the actual sys- tem. The algorithm described provides high control performance when using it with classical PID-based cascade temperature control of jacketed batch reactors;however, it can be used with or as part of other types of controllers, for ex- ample, model-based temperature controllers. The algorithm can be used in the case of systems where only two as well as where three temperature levels are used for temperature control. Besides the switching between the modes of opera- tion and calculating the value of the manipulated variable, one of the most important functions of the split-range algo- rithm is to keep the sign of the gain of the controlled system unchanged. However, with a more system-specific split-range solution, not only can the sign of the gain be kept unchanged, but the gain can also be constant or less de- pendent on the state of the system. Using this solution, the design of the PID controller becomes simpler and can be implemented in existing systems without serious changes.

基于PCS7的BATCH控制在生物制药单抗生产中的应用

随着生物制药的发展,单抗药物的生产越来越朝着系统化、集成化、数字化的方向发展。针对单抗药物复杂的工艺生产过程和配方多变的生产方式。本文采用西门子PCS7全集成DCS系统,利用符合ISA-S88标准的批量控制软件和配方管理,研究单抗控制方案和第三方设备集成方案,设计出适用于单抗生产的批次控制DCS系统,满足其生产工艺、参数多变的柔性化需求和数据完整性要求。

面向云存储的属性基双边访问控制方案

针对目前云存储中细粒度双边访问控制机制安全模型较弱且外包解密结果缺乏验证的问题,提出了一种面向云存储数据的属性基双边访问控制方案。首先,提出了自适应安全可验证外包双边CP-ABE的形式化定义和安全模型;其次,以此为基础并结合批量可验证技术在合数阶群上设计了双边访问控制方案,支持数据拥有者与数据使用者同时为对方定义访问策略;最后,安全性分析表明,所提方案在自适应安全模型下针对选择明文攻击与选择消息攻击是不可区分的和存在性不可伪造的。实验结果显示,所提方案减轻了用户端的匹配、解密以及验证阶段的计算开销。

降低间歇法聚丙烯灰分的控制措施

间歇式液相本体法聚丙烯工艺是20世纪70年代我国自行开发的,该工艺具有流程短、设备简单、投资小、见效快的特点。但是随着人们对聚丙烯质量和数量要求的不断提升(该工艺同时存在着产品质量不稳定、品种单一、成本偏高的缺点),特种牌号聚丙烯成为当下市场紧缺的产品,其中超低灰聚丙烯成为人们越来越关注的焦点,考验着聚合釜的操作灵活性。本文介绍了降低间歇法聚丙烯灰分的控制措施。